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Big Data & Hadoop

Summary


Big Data and Hadoop training course is designed to provide knowledge and skills to become a successful Hadoop Developer. In-depth knowledge of concepts such as Hadoop Distributed File System, Hadoop Cluster, Map-Reduce, Hbase, Hive, Pig etc. will be covered in the course.

The detailed course outline is a below:

The Motivation for Hadoop

    • Problems with traditional large-scale systems
    • Requirements for a new approach

Hadoop: Basic Concepts

    • Map-side join
    • Reduce-Side join

Introduction

    • An Overview of Hadoop
    • The Hadoop Distributed File System
    • Hands-On Exercise
    • How MapReduce Works
    • Hands-On Exercise
    • Anatomy of a Hadoop Cluster
    • Other Hadoop Ecosystem Components

Writing a MapReduce Program

    • The MapReduce Flow
    • Examining a Sample MapReduce Program
    • Basic MapReduce API Concepts
    • The Driver Code
    • The Mapper
    • The Reducer
    • Hadoop’s Streaming API
    • Using Eclipse for Rapid Development
    • Hands-on exercise
    • The New MapReduce API

Common MapReduce Algorithms

    • Sorting and Searching
     Indexing
    • Word Co-Occurrence
    • Hands-On Exercise.

PIG Concepts

    • Data loading in PIG.
    • Data Extraction in PIG.
    • Data Transformation in PIG.
    • Hands on exercise on PIG.

Hive Concepts

    • Hive Query Language.
    • Alter and Delete in Hive.
    • Partition in Hive.
    • Indexing.
    • Joins in Hive.Unions in hive.
    • Industry specific configuration of hive parameters.
    • Authentication & Authorization.
    • Statistics with Hive.
    • Archiving in Hive.
    • Hands-on exercise

Working with Sqoop

    • Introduction.
    • Import Data.
    • Export Data.
    • Sqoop Syntaxs.
    • Databases connection.
    • Hands-on exercise

Working with Flume

    • Introduction.
    • Configuration and Setup.
    • Flume Sink with example.
    • Channel.
    • Flume Source with example.
    • Complex flume architecture.

OOZIE Concepts
HUE Concepts
Reporting Tools







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